# An introduction to Docker for reproducible research, with examples from the R environment

arXiv: https://arxiv.org/abs/1410.0846 · HTML: https://mlsanity.com/paper/1410.0846/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2014.

## Built on by (most cited first, 26 of 26)

- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems](https://mlsanity.com/paper/2101.09385/) (2021)
- [A Survey for Federated Learning Evaluations: Goals and Measures](https://mlsanity.com/paper/2308.11841/) (2023)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Privacy and Trust Redefined in Federated Machine Learning](https://mlsanity.com/paper/2103.15753/) (2021)
- [A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions](https://mlsanity.com/paper/2103.03610/) (2021)
- [OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics](https://mlsanity.com/paper/2203.00403/) (2022)
- [Improving the Reproducibility of Deep Learning Software: An Initial Investigation through a Case Study Analysis](https://mlsanity.com/paper/2505.03165/) (2025)
- [DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model](https://mlsanity.com/paper/2212.01302/) (2022)
- [Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI](https://mlsanity.com/paper/2405.18753/) (2024)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [A Guide to Computational Reproducibility in Signal Processing and Machine Learning](https://mlsanity.com/paper/2108.12383/) (2021)
- [Reproducible and Portable Big Data Analytics in the Cloud](https://mlsanity.com/paper/2112.09762/) (2021)
- [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024)
- [Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows](https://mlsanity.com/paper/2603.06394/) (2026)
- [ClaimReceipt: Verifying Evidence Sufficiency and Coverage in Agent Evaluations](https://mlsanity.com/paper/2609.01992/) (2026)
- [Reliable validation of Reinforcement Learning Benchmarks](https://mlsanity.com/paper/2203.01075/) (2022)
- [A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams](https://mlsanity.com/paper/2312.07682/) (2023)
- [Requirements for Quality Assurance of AI Models for Early Detection of Lung Cancer](https://mlsanity.com/paper/2502.17639/) (2025)
- [Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies](https://mlsanity.com/paper/2510.03305/) (2025)
- [Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC](https://mlsanity.com/paper/2605.08396/) (2026)
- [Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models](https://mlsanity.com/paper/2609.08418/) (2026)
- [Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software](https://mlsanity.com/paper/2610.00425/) (2026)
- [An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials](https://mlsanity.com/paper/2610.03505/) (2026)

## Abstract (arXiv)

As computational work becomes more and more integral to many aspects of scientific research, computational reproducibility has become an issue of increasing importance to computer systems researchers and domain scientists alike. Though computational reproducibility seems more straight forward than replicating physical experiments, the complex and rapidly changing nature of computer environments makes being able to reproduce and extend such work a serious challenge. In this paper, I explore common reasons that code developed for one research project cannot be successfully executed or extended by subsequent researchers. I review current approaches to these issues, including virtual machines and workflow systems, and their limitations. I then examine how the popular emerging technology Docker combines several areas from systems research - such as operating system virtualization, cross-platform portability, modular re-usable elements, versioning, and a `DevOps' philosophy, to address these challenges. I illustrate this with several examples of Docker use with a focus on the R statistical environment.